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My main intent for finally beginning to publicize the #KPI data was to see if I had “something” or “nothing”. I had been utilizing the formula on college basketball rankings while compiling data and making schedules behind the scenes for close to a decade. As the rankings were taken more seriously in private, I became more curious. What did I have? Was it a fun hobby or was it a resource with value?

Slowly but surely, I have learned that I have “something” and am refining the scope of what that “something” is. I held back a significant amount of data and information partly because I wanted to see what worked and what didn’t work to drive interest and partly to stay clear from any perceived bias coming from my role at Michigan State. This first year served very much as a trial run and was a huge success with views in the tens of thousands. Hits on the site peaked as Selection Sunday approached. Thank you for making me feel so good about my decision.

I’ll also admit that while I had a written plan going into last fall, I strayed from the plan as I learned what people were reading most. Ironically, I had no intention of tracking college basketball statistical trends (or more specifically the”foul/free throw data”) to the extreme level that I did. Those updates proved invaluable for many and helped drive the national conversation throughout the season. That data opened doors and built contacts I never saw coming. How I compiled the data in the existing and exhaustive ranking database also opened up literally millions of new data points that proved invaluable and provided me with plenty of smiles and ‘aha’ moments. Studies on scoring differences based on afternoon or evening games in college basketball, scheduling trends in college football and travel distances affecting expected output are now possible and relatively simple, and that is just the tip of the iceberg.

The “Schedule” worksheet in the 42.6 MB Excel file tracking data on the 5,947 college basketball games involving a D-I team has grown to contain 2.3 million cells of data. That’s one worksheet in one sport. I am working toward a more user-friendly way to both compile the data and allow people to access it (rather than PDF files and Word-based blog entries). I’m hopeful readers will be have the ability to easily customize data in the future and am hopeful the followers continue following.

The Future of the #KPI is exciting. The formula is capable of ranking teams and games in any sport that has a result and a score. I need to further automate the data to allow for rankings in more sports, both college and professional (ahem, computer programmer, maybe?). Admittedly, I entered a LOT of numbers manually this past year before I could let Excel do its thing. With all due respect to the RPI that has been used for more than 30 years, I believe the #KPI is more accurate as an overall ranking system while also providing more specific data for game by game analysis. The ability to decipher quality wins and identify outliers is invaluable. With the football committee starting this fall, data will be critical as the smallest details will separate the fourth and fifth best teams. While the RPI only gives a final “what”, the #KPI gives what, why and how come. The #KPI can answer questions the RPI can’t.

I am also running analysis to possibly make some slight enhancements to the formula. In order to improve accuracy, I’m playing with the home/away/neutral adjustments in order to possibly use percentile-based scaling of the adjustments based on the level of opponent (rather than a set adjustment and scaling for the strength of opponent only later). This would emphasize the quality of opponent slightly more than it is now. I’m also toying with some data relative to margin as well. Currently, margin is factored as a multiplier based on twice the percentage of total points scored. I want to minimize some extreme outliers. The same formula is used across multiple sports. I’m debating that topic with myself as well. (Suggestions are always welcome at kpauga@gmail.com). Once I am comfortable that the formula is properly protected, I will be releasing the exact formula and rationale.

#KPI Sports Scheduling has always been a serious passion of mine and has always been part of the long-term plan of the #KPI brand. Schedules can be made for both competitive and geographic equity. I use computers but also incorporate some of the permutations by hand in order to create the perfect schedules. Scheduling is a fine art more than simply plugging teams into a rubric. Television, team travel, rivalries and so much more can be factored depending on a conference or league’s preferences. Thank you to those who have already given me the opportunity to prove I can bring value to their schedules (wink, wink). Think I’m crazy? Send me your data and I’ll produce a schedule for you. I’m not kidding. My goal remains to create the Major League Baseball schedule someday soon – the most complicated and largest undertaking in professional sports.

There were plenty of fun, anecdotal moments from the #KPI throughout this year too. Waking up on November 11 to the initial foul/free throw/scoring data going viral was a surprise. Watching the Arizona-San Diego State game the following Thursday and seeing “KPI” on a major TV network was special. Determining the math by hand as to how big an outlier the Boston College-Syracuse score was on the back of an itinerary while sitting on the floor of a West Lafayette hotel was unique. There were consecutive all-nighters at the Big Ten Tournament getting data updated after west coast games were done and my MSU responsibilities were completed for the night. Numerous texts and calls came that last weekend asking “where is my team in the KPI?” My response was “I’m glad you care!” Answering Tracy Wolfson’s question during warmups of the Big Ten Tournament championship game and telling her I thought MSU would be a “4 seed in Spokane” only to have it come true made for a few laughs. There were several Twitter alerts on my phone with new followers I was honored to have and won’t take for granted. All in all, it was incredibly fun and challenging.

Thank you to the readers, the followers and those who have given invaluable advice during this first year (my favorite coming in December when I was told to keep it simple and at a level where an average basketball fan can understand the numbers). I’m humbled as to how people have taken it seriously and have come along for the ride.

I will be working this summer in preparation for #KPI 2.0 in August (and of course, suggestions are always welcome). I’m excited to see what the future will bring…

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· The four Final Four teams combined to go 124-27 (.821), but went 62-21 (.747) in conference games/including conference tournaments and 62-6 (.912) in non-conference games/including NCAA Tournament. Of the 6 non-conference losses the four schools combined for, two were vs each other (Florida’s two losses). The other 4? Kentucky vs Michigan State, Kentucky vs Baylor, Kentucky vs North Carolina, Connecticut vs Stanford.
· Non-Conference SOS (including NCAA Tournament games) is top-6 for all 4 Final Four teams (calculated by the average KPI of each opponent)
· Florida has played each of the other Final Four teams. None of the other 3 teams have played each other this year. UConn and Wisconsin are the only two teams to beat Florida. Florida swept Kentucky.
· Florida leads the country with 19 road/neutral wins (19-2). Wichita State won 18 such games, followed by Michigan State and Stephen F. Austin with 17 each.
· Northwestern’s win at Wisconsin was the 3rd largest outlier game of the season by KPI formula (1-Boston College at Syracuse, 2-Illinois-Chicago at Milwaukee).
· Wisconsin was the only team not to play in their conference tournament championship game. Florida was the only team to win their conference tournament. Florida was also the only team with a regular season conference championship.

Offensive Production (Calculated as difference between a team’s PPP to date and the average of all their opponents defensive PPP): 4-Wisconsin +.164, 12-Kentucky +.122, 22-Florida +.105; 40-UConn +.080

Defensive Production (Calculated as difference between a team’s defensive PPP to date and the average of all their opponents offensive PPP): 4-Florida +.159; 11-UConn +.113; 38-Kentucky +.081; 40-Wisconsin +.079

NCAA Tournament Trends
Through the regional finals of the 2013 and 2014 NCAA Tournaments (64 games):
Steals are down 13.4% (.84 per game)
Turnovers are down 14.5% (1.75 per game)
Scoring is up 4.6% (3.03 PPG), up 3.4% per minute (factoring in increased number of OT games this year)
Fouls are up 4.1% (.70 per game), up 2.9% per minute, FTA are up 5.2% (.98 per game), up 4.0% per minute.
FG% is up from 42.3% to 44.2%, FT% is up from 71.2% to 72.7%
Points Per Possession are up 5.8% (from 1.006 to 1.064)

Of the 3.03 PPG increase, 2.25 points come from more made 2’s (72.0% of increase), 0.99 points from more made FT’s (31.7% of increase) and made 3’s are down 0.21 PPG (-6.7% of increase).

The oversimplified narrative: Scoring is up because (1) about 1 would be steal/turnover is being called a foul per team and (2) FG% is up – because an extra foul per game is being called on what may have been a missed shot.

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All season long, the #KPI has examined statistical trends as it relates to the rules of college basketball. The NCAA Tournament, where the stakes are the highest, is no different.

Scoring tends to fall come NCAA Tournament time. Over the last three years (since the field expanded to 68), NCAA Tournament scoring has dropped 1.50 PPG in 2011 (down 2.2% from regular season), 1.55 PPG (down 2.3% from regular season) and 1.72 PPG (down 2.6% from regular season).

PPG in NCAA Tournament

3-yr Avg

2013

2012

2011

First Round

65.5

67.5

64.5

64.5

Second Round

65.9

64.5

66.4

66.7

Third Round

67.4

66.7

65.0

70.4

Sweet 16

68.7

67.4

68.3

70.4

Elite Eight

68.8

64.5

73.3

68.8

Final Four

63.0

64.3

64.0

60.8

Championship

63.0

79.0

63.0

47.0

All NCAA Games

66.6

65.8

66.5

67.6

Regular Season

67.5

68.0

69.1

Scoring Margin has averaged 11.3 PPG over the last three NCAA Tournaments. Over a three-year period, margins in the second round (13.2 PPG) and third round (10.8 PPG) have been greatest as one might expect. The average margin for each round of the tournament over the last three seasons:

Margin in NCAA Tournament

3-yr Avg

2013

2012

2011

First Round

9.2

9.0

7.5

11.0

Second Round

13.2

15.2

11.1

13.2

Third Round

10.8

12.1

9.7

10.4

Sweet 16

9.0

8.8

8.4

10.0

Elite Eight

10.0

15.5

9.0

5.5

Final Four

4.7

4.5

5.0

4.5

Championship

8.7

6.0

8.0

12.0

All NCAA Games

11.3

12.9

9.9

11.3

In games deemed upsets (won by the lower seed), the average margin has been 7.9 PPG over the last three seasons. When the higher seed wins, the average margin is 12.8 PPG.

There were 24 “upsets” (games won by the lower seeded team, wearing the dark uniforms) in 2013, 18 “upsets” in 2012 and 22 “upsets” in 2011. 32% of games are “upsets” while 68% of games are won by the better seed.

Scoring tends to fall during the postseason because the number of possessions in a game tend to fall. In conference tournament action this year, scoring fell to 68.2 PPG (compared to 69.7 PPG in conference games and 72.1 PPG in non-conference). The number of possessions per game, per team fell to 65.1 possessions/game in conference tournaments (from 66.5 in conference games and 69.4 during the non-conference). Most other data was consistent on a per-possession basis and reflected the decrease in possessions, and hence longer possessions. Full data from the 2013-14 season is also available at kpisports.net.

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Hello from beautiful Spokane. Admittedly, it’s been a whirlwind couple of days as the #KPI computers have made their way from Indianapolis Sunday, to East Lansing late Sunday night via bus and to Spokane late Monday. With Michigan State playing in the Big Ten Tournament championship game, there was no opportunity to update data prior to the Selection Show on Sunday (heck, I didn’t even make it to a television until the South Region was well announced!).

I felt badly that I didn’t get anything posted before Sunday’s Selection Show (though it was quite obvious that win or lose Sunday, nothing was getting posted). I want to thank the folks at http://bracketmatrix.com/ for choosing the #KPI to be part of the formula. My ranking there is based as of math as of 7 a.m. on Sunday. No human element, no update on Sunday. And that’s ok and understood. Their site is an incredible tool.

I have been keeping a 1-68 list separate from the #KPI. That list got 67/68 teams correct. 40 teams were seeded correctly, 64 were within one seed line and the other 3 in that field were off by two seeds. The average difference in seeding was .448. I won’t publish that list until after the NCAA Tournament is over for several reasons.

Again, thank you for the incredible interest in the #KPI this year. The interest has far exceeded my expectations and has been quite humbling. It was quite fun getting texts and calls from people asking what their #KPI would be if they won this game or made it how far in their conference tournament. There is still plenty more data coming as the month progresses. I hope readers continue to be interested.

19 of 31 conference tournaments were won by someone other than the No. 1 seed.

Scoring was up 2.87 PPG (up 4.39%) from 65.3 PPG to 68.2 PPG in conference tournament play this year when compared to 2012-13. Week 19 (March 10-16) was the lowest scoring week of the season (67.4 PPG). The next lowest total was 68.9 PPG (Jan 13-19). Week 19 was also the lowest of the season for possessions (64.6), field goal attempts (53.9), 3-pt field goal attempts (17.7), assists (11.4), steals (5.6) and turnovers (11.3). As has been said all year, the number of possessions (hence pace of play) has a lot to do with the decreases we have seen week to week.

Should bracketologists predict what the committee will do or what they think it should do? Two different things. @ESPNLunardi? @jppalmCBS?

Dissecting the #KPI formula how it relates to the actual NCAA Tournament seeding:

THE GOOD:

The top-8 teams in the #KPI all landed a 1 or 2 seed. Arizona, Florida, Wichita State, Wisconsin, Villanova and Michigan were all on the exact seed line. Kansas was a No. 1 seed in the math while Virginia was a No. 2 seed. They were flipped.

All 16 teams seeded 1-4 appeared in the top-17 of the #KPI. The lone exception was Mountain West Tournament champion New Mexico, who earned a 7 seed.

Of the last 18 AQ teams in the tournament (13 seed or below), 14 of 18 were seeded correctly. Western Michigan and Delaware were swapped in the #KPI, as were Eastern Kentucky and North Carolina Central.

50.8% of teams who made the tournament were placed on their exact seed line by #KPI math. 77.8% were exact or within one seed line while 88.9% were exact or within two seed lines.

The average differential between #KPI and tournament seeds was 0.825.

The last four teams below the cut line who made the NCAA Tournament: Tennessee, N.C. State, Iowa and Xavier (in that order). All four of those teams are playing in Dayton Tuesday and Wednesday night. The eight teams playing in Dayton are the exact four lowest #KPI AQ teams and the exact four lowest #KPI at-large teams based on who made the tournament.

Between the NCAA and NIT, the #KPI got 96 of 100 teams in the two tournaments (96%). The #KPI had Georgia and LSU as the first two teams out of the NIT, while West Virginia and Indiana State were a few spots back.

THE NOT SO GOOD

I’ll admit that I wanted badly to land 68/68 teams this year. It didn’t happen. Because of how some things fell, I became at the mercy of certain teams winning their conference tournaments. Green Bay, Louisiana Tech and Belmont all received automatic bids to the NIT, but were actually above my cut line. Green Bay’s win over Virginia, Louisiana Tech’s win at Oklahoma and Belmont’s win at North Carolina were all great wins, but only enough to inflate their #KPI, not enough to get them in the tournament.

North Dakota State, Harvard and Stephen F. Austin were higher in the #KPI than the 12 seeds that each team received.

The #KPI math missed on New Mexico – the largest outlier at a difference of 4 seed lines. Remove New Mexico from the equation, and the #KPI had 14 of the top 16 teams seeded correctly (with only Virginia and Kansas swapped on the first four lines).

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Happy Selection Sunday! There is a lot of data to dig through below. Anecdotes throughout Sunday can be found on Twitter at @KPIsports (though I’ll be slightly preoccupied during the Big Ten Tournament championship game at 3:30 p.m. ET). Thank you for following along!

Pay attention to the #KPI value more than the ranking as we approach the Selection Show. For example, Nebraska (No. 51) is in nearly a dead tie for one of the final at-large spots as of Sunday mornings. There are several clusters among seeding, including a group of six teams separated by .005 and a group of five teams separated by .002.

16 conference tournaments have been won by someone other than the No. 1 seed. Five more will play Sunday. The Atlantic 10 No. 1 seed (Saint Louis) was eliminated, meaning the total will hit at least 17 of the 31 conference tournaments.

As in past years, conference tournament scoring trended downward from regular season numbers. Week 19 (Mar 10-16) produced a season-low 67.4 PPG (up from 66.9 PPG during Week 19 in 2012-13). The previous low this year was 68.9 PPG (Jan 13-19). Possessions per game were at a season-low 64.7 per game. Field goal attempts (53.8), 3-pt attempts (17.7), steals (5.6) and turnovers (11.4) were at season low averages this week.

Bracket Trends to Look For:

Florida is likely the No. 1 seed in the South (Orlando/Memphis), Arizona the No. 1 in the West (San Diego/Anaheim) and Wichita State the No. 1 in the Midwest (St. Louis/Indianapolis). The fourth one seed (likely headed to the East Region at Madison Square Garden) could create the need for a contingency bracket based on the result of the Big Ten championship game.

Look for Florida and Wichita State to play the winner of the play-in games. Florida would play the winner of Tuesday’s early game Thursday in Orlando while Wichita State would play the winner of Wednesday’s first round game Friday in St. Louis. The fourth number one seed could play this group if the committee prefers the geography of Milwaukee. The 16 seeds likely to play in Dayton are Albany, Cal Poly, Mount St. Mary’s and Texas Southern.

With a few new bracketing principles this year, look for seeding to be more in line with what people project (since there will be fewer seed line adjustments for bracketing). Also, look for geography to play a bigger role in regional placement without conference matchups happening all that early, all that often.

There are no bid stealers left to play in any of Sunday’s games. The only question remaining relative to who is in the field comes from the Sun Belt championship (Louisiana-Lafayette vs. Georgia State). Look for Georgia State in the No. 13 seed range with a win. If ULL wins, they could fall closer to a low No. 14/high No. 15 seed.

Much like Middle Tennessee in 2013, there may be a team from a non-power conference who plays in Dayton on Tuesday or Wednesday. Southern Miss, Toledo and Louisiana Tech each won 27 games. Green Bay won 24 games, including a home win over ACC regular season champion Virginia. The #KPI likes teams who win 27 games and as a result the formula is more likely to miss on a few of those teams. Some of the power conference teams at the top of the pile on the NIT list are very much in play for NCAA Tournament bids based on the math.

The first round games for at-large teams are likely to fall one each at the 11 and 12 seed lines. The 12 seed will feed into the one 4/5 bracket that doesn’t play the first weekend out west (2 pods in Spokane, 1 pod in San Diego) while the other will play on the 11 seed line to avoid a western travel schedule.

Look for the 4/5 and 3/6 pods to be filled by a lot of Spokane and San Antonio. There could be a left over spot from San Diego, Buffalo and Orlando as well. Raleigh (Virginia/Duke), Milwaukee (Michigan/Wisconsin) and St. Louis (Wichita State/Kansas) are likely to have pods with higher seeds.

Including Green Bay and Belmont, there are 11 automatic qualifiers into the NIT (leaving only 21 at-large teams). There will likely be several high profile schools left out of the NIT that will draw people’s attention.

Games missed by key players will be a hot topic Sunday night for several teams – including some players who may not participate in the NCAA Tournament.

Another hot topic: winning and losing streaks. Several teams had significant winning and losing streaks that produced several outlier results based on #KPI data.

The Big 12 is likely to get 7 of 10 teams (70%) into the NCAA Tournament field. It could also be a big day for the Atlantic 10.

#KPI NCAA Tournament Field of 68(as of Sunday, March 16, 7 a.m. ET): Automatic bids from the 32 conferences are determined by the top remaining seed if the AQ has not already been decided. This list is comprised purely by math with no scrubbing.

Home teams are 3,518-1,691 (.675). There have been 587 games (10.1% of all games) played at a neutral site, including conference tournaments. 56.7% of all games are conference games or conference tournament games to date.

7.6% of all games involving a Division I team are currently against Non-Division I teams. Division I teams are 425-14 (.968) in those games by a margin of 90.2 PPG to 60.5 PPG. Non-Division I games also count in the #KPI rankings. All games against Non-Division I teams count as one opponent (currently No. 350 in the #KPI).

Partly due to the multitude of upsets in smaller conferences, Southern Miss, Toledo, Green Bay, Louisiana Tech and Belmont look better in the #KPI.

As a result, teams like Tennessee (numbers put them just out) are looking better.

#KPI NCAA Tournament Field of 68 (as of Saturday, March 15, 8:30 p.m. ET): Automatic bids from the 32 conferences are determined by the top remaining seed if the AQ has not already been decided. This list is comprised purely by math with no scrubbing.

#KPI NIT Tournament Field of 32 (as of Saturday, March 15, 8:30 p.m. ET): Automatic bids are awarded to teams who won their regular season conference title, but did not win their conference tournament.